Overview

Dataset statistics

Number of variables11
Number of observations8859806
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory743.5 MiB
Average record size in memory88.0 B

Variable types

Numeric11

Alerts

LongitudAcc is highly correlated with Engine Load and 1 other fieldsHigh correlation
EngineSpeed is highly correlated with EngineAirInletPressure and 2 other fieldsHigh correlation
Fuel Rate is highly correlated with Engine Load and 2 other fieldsHigh correlation
Engine Load is highly correlated with Boost Pressure and 2 other fieldsHigh correlation
Boost Pressure is highly correlated with Engine Load and 2 other fieldsHigh correlation
EngineAirInletPressure is highly correlated with EngineSpeed and 3 other fieldsHigh correlation
AcceleratorPedalPos is highly correlated with EngineSpeed and 3 other fieldsHigh correlation
VehicleSpeed is highly correlated with EngineSpeedHigh correlation
BrakePedalPos is highly correlated with AcceleratorPedalPosHigh correlation
Fuel Rate is highly skewed (γ1 = 52.46319414) Skewed
Timestamp has unique values Unique
LongitudAcc has 2083141 (23.5%) zeros Zeros
EngineSpeed has 175858 (2.0%) zeros Zeros
Fuel Rate has 2091514 (23.6%) zeros Zeros
Engine Load has 2101609 (23.7%) zeros Zeros
Boost Pressure has 415773 (4.7%) zeros Zeros
AcceleratorPedalPos has 3574373 (40.3%) zeros Zeros
VehicleSpeed has 1260521 (14.2%) zeros Zeros
BrakePedalPos has 7210811 (81.4%) zeros Zeros

Reproduction

Analysis started2022-11-23 15:56:39.345952
Analysis finished2022-11-23 16:07:06.873871
Duration10 minutes and 27.53 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

Timestamp
Real number (ℝ≥0)

UNIQUE

Distinct8859806
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.683501885 × 1010
Minimum4.757909408 × 1010
Maximum1.114190064 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.6 MiB
2022-11-23T17:07:06.984012image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum4.757909408 × 1010
5-th percentile4.942679251 × 1010
Q15.857681099 × 1010
median6.704562371 × 1010
Q37.504463751 × 1010
95-th percentile8.330114331 × 1010
Maximum1.114190064 × 1011
Range6.383991232 × 1010
Interquartile range (IQR)1.646782652 × 1010

Descriptive statistics

Standard deviation1.054857865 × 1010
Coefficient of variation (CV)0.157830114
Kurtosis-0.8350820478
Mean6.683501885 × 1010
Median Absolute Deviation (MAD)8289939704
Skewness0.02679453497
Sum5.92145301 × 1017
Variance1.112725115 × 1020
MonotonicityStrictly increasing
2022-11-23T17:07:07.129692image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.757909408 × 10101
 
< 0.1%
7.263572794 × 10101
 
< 0.1%
7.263574992 × 10101
 
< 0.1%
7.263574808 × 10101
 
< 0.1%
7.263574695 × 10101
 
< 0.1%
7.263574506 × 10101
 
< 0.1%
7.263574394 × 10101
 
< 0.1%
7.263574291 × 10101
 
< 0.1%
7.263574096 × 10101
 
< 0.1%
7.2635738 × 10101
 
< 0.1%
Other values (8859796)8859796
> 99.9%
ValueCountFrequency (%)
4.757909408 × 10101
< 0.1%
4.757909524 × 10101
< 0.1%
4.757909633 × 10101
< 0.1%
4.757909708 × 10101
< 0.1%
4.757909824 × 10101
< 0.1%
4.757909908 × 10101
< 0.1%
4.75791001 × 10101
< 0.1%
4.757910113 × 10101
< 0.1%
4.757910227 × 10101
< 0.1%
4.757910413 × 10101
< 0.1%
ValueCountFrequency (%)
1.114190064 × 10111
< 0.1%
1.114190052 × 10111
< 0.1%
1.114190041 × 10111
< 0.1%
1.114190035 × 10111
< 0.1%
1.114190023 × 10111
< 0.1%
1.114190011 × 10111
< 0.1%
1.114190004 × 10111
< 0.1%
1.114189993 × 10111
< 0.1%
1.114189982 × 10111
< 0.1%
1.114189975 × 10111
< 0.1%

WetTankAirPressure
Real number (ℝ≥0)

Distinct196
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.23041809
Minimum0
Maximum13.44525
Zeros21990
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size67.6 MiB
2022-11-23T17:07:07.287513image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9.99775
Q110.82515
median11.37675
Q311.8594
95-th percentile12.34205
Maximum13.44525
Range13.44525
Interquartile range (IQR)1.03425

Descriptive statistics

Standard deviation1.130928062
Coefficient of variation (CV)0.1007022226
Kurtosis34.95526587
Mean11.23041809
Median Absolute Deviation (MAD)0.48265
Skewness-4.538502418
Sum99499325.54
Variance1.278998283
MonotonicityNot monotonic
2022-11-23T17:07:07.453393image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.23885358789
 
4.0%
11.1699353211
 
4.0%
11.37675348562
 
3.9%
11.4457347726
 
3.9%
11.51465341676
 
3.9%
11.5836337871
 
3.8%
11.7215326389
 
3.7%
11.79045324266
 
3.7%
11.10095315160
 
3.6%
11.8594314491
 
3.5%
Other values (186)5491665
62.0%
ValueCountFrequency (%)
021990
0.2%
0.06895345
 
< 0.1%
0.1379250
 
< 0.1%
0.20685216
 
< 0.1%
0.2758561
 
< 0.1%
0.34475162
 
< 0.1%
0.4137188
 
< 0.1%
0.48265155
 
< 0.1%
0.5516123
 
< 0.1%
0.62055161
 
< 0.1%
ValueCountFrequency (%)
13.445251
 
< 0.1%
13.37638
 
< 0.1%
13.3073520
 
< 0.1%
13.238449
 
< 0.1%
13.16945124
 
< 0.1%
13.1005234
 
< 0.1%
13.03155659
 
< 0.1%
12.96261694
 
< 0.1%
12.893654128
< 0.1%
12.82475858
0.1%

LongitudAcc
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct125
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.03670183072
Minimum-7.6
Maximum13
Zeros2083141
Zeros (%)23.5%
Negative3701960
Negative (%)41.8%
Memory size67.6 MiB
2022-11-23T17:07:07.623685image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum-7.6
5-th percentile-1
Q1-0.2
median0
Q30.2
95-th percentile0.8
Maximum13
Range20.6
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.5439853105
Coefficient of variation (CV)-14.82174867
Kurtosis73.02868179
Mean-0.03670183072
Median Absolute Deviation (MAD)0.2
Skewness2.618388325
Sum-325171.1
Variance0.295920018
MonotonicityNot monotonic
2022-11-23T17:07:07.781080image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02083141
23.5%
-0.1870215
9.8%
-0.2748469
 
8.4%
0.1732215
 
8.3%
0.2552313
 
6.2%
-0.3535586
 
6.0%
0.3410283
 
4.6%
-0.4368754
 
4.2%
0.4296249
 
3.3%
0.5236991
 
2.7%
Other values (115)2025590
22.9%
ValueCountFrequency (%)
-7.61
 
< 0.1%
-7.51
 
< 0.1%
-7.31
 
< 0.1%
-7.21
 
< 0.1%
-7.11
 
< 0.1%
-71
 
< 0.1%
-6.81
 
< 0.1%
-6.52
< 0.1%
-6.11
 
< 0.1%
-63
< 0.1%
ValueCountFrequency (%)
131610
< 0.1%
12.9309
 
< 0.1%
5.41
 
< 0.1%
5.22
 
< 0.1%
5.14
 
< 0.1%
54
 
< 0.1%
4.96
 
< 0.1%
4.810
 
< 0.1%
4.719
 
< 0.1%
4.630
 
< 0.1%

EngineSpeed
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct13234
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1069.539
Minimum0
Maximum8191.875
Zeros175858
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size67.6 MiB
2022-11-23T17:07:07.945382image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile595.125
Q1893
median1156.125
Q31284.125
95-th percentile1457.875
Maximum8191.875
Range8191.875
Interquartile range (IQR)391.125

Descriptive statistics

Standard deviation324.8017937
Coefficient of variation (CV)0.3036839178
Kurtosis8.085599775
Mean1069.539
Median Absolute Deviation (MAD)157.75
Skewness-0.5584022858
Sum9475908045
Variance105496.2052
MonotonicityNot monotonic
2022-11-23T17:07:08.088181image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0175858
 
2.0%
600.12542914
 
0.5%
60042852
 
0.5%
600.2542648
 
0.5%
599.87541716
 
0.5%
600.37541183
 
0.5%
599.7540929
 
0.5%
600.539257
 
0.4%
599.62538892
 
0.4%
599.536892
 
0.4%
Other values (13224)8316665
93.9%
ValueCountFrequency (%)
0175858
2.0%
22.1251
 
< 0.1%
27.6251
 
< 0.1%
31.8751
 
< 0.1%
371
 
< 0.1%
41.751
 
< 0.1%
42.251
 
< 0.1%
48.251
 
< 0.1%
48.751
 
< 0.1%
491
 
< 0.1%
ValueCountFrequency (%)
8191.875281
< 0.1%
2252.51
 
< 0.1%
2246.8751
 
< 0.1%
2245.6251
 
< 0.1%
2217.51
 
< 0.1%
2208.51
 
< 0.1%
2200.251
 
< 0.1%
2186.251
 
< 0.1%
2184.1251
 
< 0.1%
2182.8751
 
< 0.1%

Fuel Rate
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct1104
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.50851436
Minimum0
Maximum3876.198645
Zeros2091514
Zeros (%)23.6%
Negative0
Negative (%)0.0%
Memory size67.6 MiB
2022-11-23T17:07:08.282876image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.887205
median7.570816
Q320.287421
95-th percentile46.962718
Maximum3876.198645
Range3876.198645
Interquartile range (IQR)19.400216

Descriptive statistics

Standard deviation70.25923101
Coefficient of variation (CV)4.842620635
Kurtosis2879.783051
Mean14.50851436
Median Absolute Deviation (MAD)7.570816
Skewness52.46319414
Sum128542622.6
Variance4936.359542
MonotonicityNot monotonic
2022-11-23T17:07:08.432971image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02091514
 
23.6%
3.312232108457
 
1.2%
3.37137998713
 
1.1%
3.25308595916
 
1.1%
3.43052671520
 
0.8%
3.19393866671
 
0.8%
3.96284964099
 
0.7%
3.90370259499
 
0.7%
4.02199658611
 
0.7%
4.08114349824
 
0.6%
Other values (1094)6094982
68.8%
ValueCountFrequency (%)
02091514
23.6%
0.0591478426
 
0.1%
0.1182948197
 
0.1%
0.17744110419
 
0.1%
0.23658814415
 
0.2%
0.29573512906
 
0.1%
0.35488210573
 
0.1%
0.4140299305
 
0.1%
0.4731768035
 
0.1%
0.5323236572
 
0.1%
ValueCountFrequency (%)
3876.1986452798
< 0.1%
2861.4727131
 
< 0.1%
2848.7561081
 
< 0.1%
65.061720
 
< 0.1%
65.00255354
 
< 0.1%
64.94340672
 
< 0.1%
64.88425985
 
< 0.1%
64.82511296
 
< 0.1%
64.76596571
 
< 0.1%
64.70681887
 
< 0.1%

Engine Load
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct202
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.32693887
Minimum0
Maximum106.5
Zeros2101609
Zeros (%)23.7%
Negative0
Negative (%)0.0%
Memory size67.6 MiB
2022-11-23T17:07:08.598029image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.5
median23.5
Q342.5
95-th percentile91
Maximum106.5
Range106.5
Interquartile range (IQR)40

Descriptive statistics

Standard deviation27.3352885
Coefficient of variation (CV)0.9320880237
Kurtosis0.236917458
Mean29.32693887
Median Absolute Deviation (MAD)20
Skewness0.960540188
Sum259830989
Variance747.2179972
MonotonicityNot monotonic
2022-11-23T17:07:08.746381image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02101609
 
23.7%
100283883
 
3.2%
19.5178000
 
2.0%
19167079
 
1.9%
20133139
 
1.5%
23126156
 
1.4%
23.5122324
 
1.4%
18.5113249
 
1.3%
22.5112198
 
1.3%
24104178
 
1.2%
Other values (192)5417991
61.2%
ValueCountFrequency (%)
02101609
23.7%
0.537624
 
0.4%
127249
 
0.3%
1.520752
 
0.2%
219525
 
0.2%
2.517843
 
0.2%
319029
 
0.2%
3.518544
 
0.2%
420337
 
0.2%
4.519153
 
0.2%
ValueCountFrequency (%)
106.51
 
< 0.1%
100283883
3.2%
99.58034
 
0.1%
998782
 
0.1%
98.510146
 
0.1%
989035
 
0.1%
97.58970
 
0.1%
978570
 
0.1%
96.58549
 
0.1%
968623
 
0.1%

Boost Pressure
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct206
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2505231002
Minimum0
Maximum1.76669
Zeros415773
Zeros (%)4.7%
Negative0
Negative (%)0.0%
Memory size67.6 MiB
2022-11-23T17:07:08.900838image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.008618
Q10.060326
median0.137888
Q30.34472
95-th percentile0.887654
Maximum1.76669
Range1.76669
Interquartile range (IQR)0.284394

Descriptive statistics

Standard deviation0.2924572663
Coefficient of variation (CV)1.167386425
Kurtosis4.200984102
Mean0.2505231002
Median Absolute Deviation (MAD)0.112034
Skewness1.99446033
Sum2219586.066
Variance0.08553125264
MonotonicityNot monotonic
2022-11-23T17:07:09.062555image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.017236634940
 
7.2%
0415773
 
4.7%
0.008618387450
 
4.4%
0.103416333216
 
3.8%
0.112034315274
 
3.6%
0.094798312851
 
3.5%
0.120652275436
 
3.1%
0.08618264828
 
3.0%
0.025854256636
 
2.9%
0.12927226451
 
2.6%
Other values (196)5436951
61.4%
ValueCountFrequency (%)
0415773
4.7%
0.008618387450
4.4%
0.017236634940
7.2%
0.025854256636
2.9%
0.034472186385
 
2.1%
0.04309154041
 
1.7%
0.051708141626
 
1.6%
0.060326139175
 
1.6%
0.068944161306
 
1.8%
0.077562208530
 
2.4%
ValueCountFrequency (%)
1.766691
 
< 0.1%
1.75807212
 
< 0.1%
1.7494547
 
< 0.1%
1.7408364
 
< 0.1%
1.73221814
 
< 0.1%
1.723610
 
< 0.1%
1.71498213
 
< 0.1%
1.70636435
< 0.1%
1.69774652
< 0.1%
1.68912864
< 0.1%

EngineAirInletPressure
Real number (ℝ≥0)

HIGH CORRELATION

Distinct105
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.6404867
Minimum32
Maximum510
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.6 MiB
2022-11-23T17:07:09.227233image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum32
5-th percentile102
Q1108
median116
Q3136
95-th percentile190
Maximum510
Range478
Interquartile range (IQR)28

Descriptive statistics

Standard deviation29.34558127
Coefficient of variation (CV)0.2317235351
Kurtosis5.100646401
Mean126.6404867
Median Absolute Deviation (MAD)12
Skewness2.044736792
Sum1122010144
Variance861.16314
MonotonicityNot monotonic
2022-11-23T17:07:09.374889image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
104824398
 
9.3%
112706514
 
8.0%
102681318
 
7.7%
114586280
 
6.6%
110579524
 
6.5%
106408526
 
4.6%
116404652
 
4.6%
108373868
 
4.2%
118287958
 
3.3%
120249855
 
2.8%
Other values (95)3756913
42.4%
ValueCountFrequency (%)
321
 
< 0.1%
3450
< 0.1%
505
 
< 0.1%
5220
 
< 0.1%
661
 
< 0.1%
684
 
< 0.1%
706
 
< 0.1%
842
 
< 0.1%
865
 
< 0.1%
882
 
< 0.1%
ValueCountFrequency (%)
510284
 
< 0.1%
50820
 
< 0.1%
2781
 
< 0.1%
27622
 
< 0.1%
27445
 
< 0.1%
272121
 
< 0.1%
270235
 
< 0.1%
268473
 
< 0.1%
266906
< 0.1%
2641394
< 0.1%

AcceleratorPedalPos
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct251
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.5999498
Minimum0
Maximum100
Zeros3574373
Zeros (%)40.3%
Negative0
Negative (%)0.0%
Memory size67.6 MiB
2022-11-23T17:07:09.527895image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median39.2
Q365.6
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)65.6

Descriptive statistics

Standard deviation34.89742951
Coefficient of variation (CV)0.9534829884
Kurtosis-1.376522202
Mean36.5999498
Median Absolute Deviation (MAD)39.2
Skewness0.2653131638
Sum324268454.8
Variance1217.830586
MonotonicityNot monotonic
2022-11-23T17:07:09.692732image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03574373
40.3%
100447363
 
5.0%
62.442550
 
0.5%
64.442117
 
0.5%
61.641812
 
0.5%
59.241640
 
0.5%
62.841187
 
0.5%
6041009
 
0.5%
58.440950
 
0.5%
5640880
 
0.5%
Other values (241)4505925
50.9%
ValueCountFrequency (%)
03574373
40.3%
0.43662
 
< 0.1%
0.83613
 
< 0.1%
1.23818
 
< 0.1%
1.63876
 
< 0.1%
23453
 
< 0.1%
2.43560
 
< 0.1%
2.84027
 
< 0.1%
3.23828
 
< 0.1%
3.63685
 
< 0.1%
ValueCountFrequency (%)
100447363
5.0%
99.69811
 
0.1%
99.29818
 
0.1%
98.89179
 
0.1%
98.410237
 
0.1%
989496
 
0.1%
97.69937
 
0.1%
97.210503
 
0.1%
96.89757
 
0.1%
96.411112
 
0.1%

VehicleSpeed
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct1077
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.2871541
Minimum0
Maximum255.97971
Zeros1260521
Zeros (%)14.2%
Negative0
Negative (%)0.0%
Memory size67.6 MiB
2022-11-23T17:07:09.851624image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q116.596594
median39.19671
Q356.69559
95-th percentile75.694374
Maximum255.97971
Range255.97971
Interquartile range (IQR)40.098996

Descriptive statistics

Standard deviation24.7398133
Coefficient of variation (CV)0.6634942757
Kurtosis-0.7112892788
Mean37.2871541
Median Absolute Deviation (MAD)19.99872
Skewness0.02613198836
Sum330356951.6
Variance612.0583623
MonotonicityNot monotonic
2022-11-23T17:07:10.008098image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01260521
 
14.2%
48.99686416579
 
0.2%
48.19613415414
 
0.2%
47.99692815392
 
0.2%
47.79381615347
 
0.2%
48.49689615314
 
0.2%
46.49702414991
 
0.2%
50.0944514919
 
0.2%
47.09464214893
 
0.2%
47.5946114844
 
0.2%
Other values (1067)7461592
84.2%
ValueCountFrequency (%)
01260521
14.2%
0.9999361811
 
< 0.1%
1.0975862344
 
< 0.1%
1.1991422526
 
< 0.1%
1.2967922888
 
< 0.1%
1.3983482984
 
< 0.1%
1.4999043240
 
< 0.1%
1.5975544561
 
0.1%
1.699113343
 
< 0.1%
1.796763549
 
< 0.1%
ValueCountFrequency (%)
255.97971274
< 0.1%
255.975804273
< 0.1%
114.5903221
 
< 0.1%
114.3911162
 
< 0.1%
114.289562
 
< 0.1%
114.0903541
 
< 0.1%
113.9927041
 
< 0.1%
113.8911483
 
< 0.1%
113.7895921
 
< 0.1%
113.6919422
 
< 0.1%

BrakePedalPos
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct237
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.194278069
Minimum0
Maximum97.6
Zeros7210811
Zeros (%)81.4%
Negative0
Negative (%)0.0%
Memory size67.6 MiB
2022-11-23T17:07:10.168629image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile20.8
Maximum97.6
Range97.6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.399784608
Coefficient of variation (CV)2.316574966
Kurtosis5.564438896
Mean3.194278069
Median Absolute Deviation (MAD)0
Skewness2.33313955
Sum28300684
Variance54.75681224
MonotonicityNot monotonic
2022-11-23T17:07:10.391901image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
07210811
81.4%
1686831
 
1.0%
15.681559
 
0.9%
16.479779
 
0.9%
17.266932
 
0.8%
16.862000
 
0.7%
15.256222
 
0.6%
17.652422
 
0.6%
1842582
 
0.5%
14.838975
 
0.4%
Other values (227)1081693
 
12.2%
ValueCountFrequency (%)
07210811
81.4%
0.426871
 
0.3%
0.812871
 
0.1%
1.29874
 
0.1%
1.67565
 
0.1%
28157
 
0.1%
2.48523
 
0.1%
2.87434
 
0.1%
3.27268
 
0.1%
3.67045
 
0.1%
ValueCountFrequency (%)
97.62
 
< 0.1%
97.2194
< 0.1%
96.813
 
< 0.1%
96.444
 
< 0.1%
961
 
< 0.1%
94.42
 
< 0.1%
945
 
< 0.1%
93.67
 
< 0.1%
92.81
 
< 0.1%
92.42
 
< 0.1%

Interactions

2022-11-23T17:06:27.885496image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
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2022-11-23T17:03:30.102610image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T17:03:52.357235image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T17:04:14.718872image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T17:04:35.680083image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T17:04:58.274402image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T17:05:21.286969image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T17:05:43.605852image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T17:06:05.588702image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T17:06:29.786593image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
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2022-11-23T17:03:10.457350image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
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2022-11-23T17:04:33.527237image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
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2022-11-23T17:05:18.974779image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
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2022-11-23T17:06:03.655709image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2022-11-23T17:06:25.947646image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Correlations

2022-11-23T17:07:10.572274image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-11-23T17:07:11.057551image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-23T17:07:11.442410image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-23T17:07:11.756746image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-23T17:07:12.148557image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-23T17:06:47.687056image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-23T17:06:51.532725image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

TimestampWetTankAirPressureLongitudAccEngineSpeedFuel RateEngine LoadBoost PressureEngineAirInletPressureAcceleratorPedalPosVehicleSpeedBrakePedalPos
04.757909e+1010.342500.2737.3757.51166935.00.043090106.044.06.1988220.0
14.757910e+1010.342500.7965.87512.53916432.50.051708106.050.48.2963440.0
24.757910e+1010.342500.61253.12517.92154141.50.086180122.067.210.7961840.0
34.757910e+1010.342500.61586.25027.62164951.00.232686134.075.213.7959920.0
44.757910e+1010.34250-0.11208.5000.0000000.00.422282132.083.214.0967540.0
54.757910e+1010.342501.31297.37534.83758367.50.310248154.082.817.7957360.0
64.757910e+1010.342500.41523.50027.20762034.00.560170140.082.821.9985920.0
74.757910e+1010.342500.11260.75014.37272130.50.293012136.084.022.4985600.0
84.757910e+1010.273551.11383.25011.17878323.00.577406158.083.624.4984320.0
94.757910e+1010.273550.91245.37539.45104980.00.361956136.081.628.5958260.0

Last rows

TimestampWetTankAirPressureLongitudAccEngineSpeedFuel RateEngine LoadBoost PressureEngineAirInletPressureAcceleratorPedalPosVehicleSpeedBrakePedalPos
88597961.114190e+1111.72150-0.21332.7500.0000000.00.560170140.078.827.2951280.0
88597971.114190e+1111.859400.51155.25032.05767468.00.318866144.074.426.3967480.0
88597981.114190e+1111.928350.51252.00031.70279264.50.387810150.073.228.9981440.0
88597991.114190e+1111.997300.41336.75027.62164951.00.456754146.069.631.1972220.0
88598001.114190e+1112.135200.01375.62520.64230337.50.430900138.064.032.1971580.0
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